Sustainable Mining of Open-Pit Coal Mines: A Study on Intelligent Strip Division Technology Based on Multi-Source Data Fusion

Shuaikang Lv, Ruixin Zhang, Yabin Tao (), Zijie Meng, Sibo Wang and Zhigao Liu
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Shuaikang Lv: School of Energy and Mining Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Ruixin Zhang: School of Energy and Mining Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Yabin Tao: School of Mining Safety, North China Institute of Science and Technology, Langfang 065201, China
Zijie Meng: School of Energy and Mining Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Sibo Wang: School of Energy and Mining Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Zhigao Liu: EACON, Beijing 100083, China

Sustainability, 2025, vol. 17, issue 20, 1-21

Abstract: The rational delineation of open-pit mining areas constitutes the core foundation for achieving safe, efficient, economical, and sustainable mining operations. The quality of this decision-making directly impacts the economic benefits experienced throughout the mine’s entire lifecycle and the efficiency of resource recovery. Traditional open-pit mining area delineation relies on an experience-driven manual process that is inefficient and incapable of real-time dynamic data optimization. Thus, there is an urgent need to establish an intelligent decision-making model integrating multi-source data and multi-objective optimization. To this end, this study proposes an intelligent mining area division algorithm. First, a geological complexity quantification model is constructed, incorporating innovative adaptive discretisation resolution technology to achieve precise quantification of coal seam distribution. Second, based on the quantified stripping-to-mining ratio within grids, a block-growing algorithm generates block grids, ensuring uniformity of the stripping-to-mining ratio within each block. Subsequently, a matrix of primary directional variations in the stripping-to-mining ratio is constructed to determine the principal orientation for merging blocks into mining areas. Finally, intelligent open-pit mining area delineation is achieved by comprehensively considering factors such as the principal direction of mining areas, geological conditions, boundary shapes, and economic scale. Practical validation was conducted using the Shitoumei No. 1 Open-Pit Coal Mine in Xinjiang as a case study in engineering. Engineering practice demonstrates that adopting this methodology transforms mining area delineation from an experience-driven to a data-driven approach, significantly enhancing delineation efficiency. Manual simulation of a single scheme previously required approximately 15 days. Applying the methodology proposed herein reduces this to just 0.5 days per scheme, representing a 96% increase in efficiency. Design costs were reduced by approximately CNY 190,000 per iteration. Crucially, the intelligently recommended scheme matched the original design, validating the algorithm’s reliability. This research provides crucial support for theoretical and technological innovation in intelligent open-pit coal mining design, offering technical underpinnings for the sustainable development of open-pit coal mines.

Keywords: open-pit coal mine; multi-source data fusion; block segment growth algorithm; geological complexity quantification; intelligent zoning division (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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